The impact of visible and invisible symptoms on employment status,work and social functioning in Multiple Sclerosis

Abstract

BACKGROUND:

Frequently diagnosed in young adulthood, multiple sclerosis (MS) and several MS-related factors can influence patients’ unemployment status and negatively affect work productivity and daily functioning.

OBJECTIVE:

We examined MS patients’ employment status and evaluated clinical features influencing it. Furthermore, we investigated patients’ burdens due to visible and invisible MS symptoms through their worsening daily functioning.

METHODS:

The study included outpatients affected by MS according to the 2010 McDonald criteria. The co-occurrence of invisible symptoms (fatigue, depression and apathy) was stated using validated, self-administered tools: Fatigue Severity Scale (FSS); Beck Depression Inventory-Second Edition (BDI-II); Apathy Evaluation Scale (AES). Impairment in daily functioning due to MS was assessed using the Work and Social Adjustment Scale (WSAS). Descriptive statistics, hierarchical regression analyses, Pearson’s correlation, and the t-test were conducted.

RESULTS:

Of the 123 participants, 52 (42.3%) were unemployed. Results showed employment to be positively associated with higher education levels (p 0.01); female gender (p 0.03) and higher disability (p 0.02) showed negative associations with employment. No associations were found between employment and fatigue or clinically relevant depressive and apathetic symptoms. High correlations were found between WSAS score and Expanded Disability Status Scale score (r = 565, p < 0.001), BDI-II score (r = 588, p < 0.001), and FSS score (r = 545, p < 0.001).

CONCLUSION:

Our study revealed physical disability’s significance in determining MS patients’ unemployment. Alternatively, invisible MS symptoms negatively affected principally patients’ social lives. Therefore, programs should be designed to improve MS patients’ work integration and daily activities.

Keywords

Physical disability disease burden work and social adjustment

1 Introduction

Multiple Sclerosis (MS) is multifaceted neurological disease with variable outcomes that mainly afflicts young adults, leading to the deterioration of several aspects of their lives [1]. The unpredictable course of disease activity, the possibility to accumulate neurological disability despite the influence of treatments, [2, 3] the co-occurrence of invisible symptoms such as fatigue, mood disorders and cognitive impairment, can adversely affects working, [4] psychosocial and many others domains, crushing the patient under the disease’s heavy burden [5]. Notoriously, work is important to life, is crucial to well being, and is a ways by which individuals define themselves. However, the presence of chronic illness can negatively affect the relationship with the work in particular in individuals with disabilities, resulting in objective or perceived employment difficulties [6, 7].

The relation between MS and unemployment is well documented, ranging from 22% to 80% [8, 9]. Several MS-related factors have been correlated with unemployment status, including the disease’s course, its duration, and neurological disability [4, 10].

Currently, growing attention is directed at the study of the impact that invisible manifestations of MS have on daily life, including especially working activity, but also home management, social and leisure activities. Hidden MS symptoms may in fact heavy affect daily living, contributing to determine a limited existence [11]. Living with MS impairs employment and socioeconomic status, [8] thus imposing financial strains on people with MS (pwMS) and their families, and also negatively affects employed patients’ work productivity and their relationships with work colleagues; [12] this might worsen as the disease progresses. In addition to working domain deterioration, MS also influence more globally the individual’s sense of self as than of own social role, [13] especially as the disease advances, the disability increases and in presence of neuropsychiatric symptoms as depression, [14] leading to psychosocial loss, and finally resulting in low quality of life and stigma [11]. This becomes a social problem because the disease affects mainly young adults during the productive stage of their lives.

The distribution of MS in Europe is heterogeneous, with variations in both prevalence and incidence rates within the countries themselves. In this regard Sardinia, the second largest Italian island located in the Mediterranean Sea, is one of the highest-risk areas for the disease in the world [15].

Considering the high frequency of MS and the anticipation of the disease onset previously reported in this island, [16] we regarded it as crucial to explore daily difficulties of pwMS with focus on employment status, to comprehend and amend potentially reversible causes of unemployment in a region with important burden of MS.

Therefore, this study was aimed to accomplish the following: (i) explore the employment status and social interactions of pwMS; (ii) evaluate which demographic and MS clinical features influence employment status, focusing on both visible and invisible MS symptoms; and (iii) analyze the attributable burden due to visible and invisible MS symptoms in worsening daily functioning at work and social adjustment over time.

2 Materials and methods

This study included outpatients affected by MS according to the 2010 McDonald criteria [17] and consequently referred for routine follow-up to the MS Center of the University of Cagliari. Participants were recruited in their order of presentation at the Center. Neurologists dedicated to the study of MS collected demographic (sex, age, and education) and clinical data (disease course, disease duration, and disability level) of pwMS. Disability was assessed using the Expanded Disability Status Scale (EDSS), the most widely used method of evaluating the degree of neurologic deficits in MS, defined by impairment of different Functional Systems (Pyramidal, Cerebellar, Brain Stem, Sensory, Bowel & Bladder, Visual, Cerebral, and Other), as previously described by Kurtzke [18] This scale states the level of neurologic disability on a scale of 0 to 10, emphasising predominantly motor and ambulation disability.

The co-occurrence of some invisible symptoms (fatigue, depression, and apathy) was indicated using self-administered tools validated in Italian language. Fatigue was measured using the Fatigue Severity Scale (FSS), [19] a nine-statement interview from which an average score is determined on a seven-point scale. The suggested cut-off point is 4, with higher scores indicating increased fatigue [19]. Depression was evaluated with the Beck Depression Inventory-Second Edition (BDI-II), [20] a self-report questionnaire including 21 items rated from 0 to 3, with a maximum total score of 63. According to manual guidelines, depressive symptomatology is indicated by scores greater than 14 [21]. Apathy was quantified using the self-reported version of the Apathy Evaluation Scale (AES-S), [22] comprising 18 items related to emotional, cognitive, behavioral, and general aspects of apathy, rated from 1 to 4; scoring is reversed for items 6, 7, and 11. Total AES-S scores range from 18 to 72, with higher scores related to greater apathy and a cut-off score of 35.5 for clinically relevant apathetic symptoms [22]. To assess the level to which MS impairs daily functioning over time (ability to work, home management, social and leisure activities, and close relationships), pwMS completed the Work and Social Adjustment questionnaire (WSAS) [23, 24]. This questionnaire aims to find out if MS influenced these activities during its course.

The maximum WSAS score is 40, and scores greater than 10 indicate significant functional impairment; conversely, the better the patient’s work and social situation is indicated by lower score [23, 24].

The only exclusion criterion was the presence of severe cognitive deterioration; therefore, pwMS with cognitive impairment were excluded owing to their inability to understand and complete self-reported surveys. If cognitive impairment was suspected but not clear, pwMS were evaluated by an expert neuropsychologist using the Brief Repeatable Battery of Neurological Tests (BRBN) [25]. Conversely, pwMS with severe motor disability or visual impairment completed questionnaires with assistance from a caregiver.

The University of Cagliari’s ethics committee approved this study, and each participant provided informed consent for the same. All questionnaires were completed in a quiet, private, and comfortable setting inside the MS clinic at the time of inclusion in the study.

3 Data analysis

The Statistical Package for the Social Sciences (SPSS) version 20.0 was used for statistical analysis. Methods of descriptive statistics, hierarchical regression analyses, Pearson’s correlation analysis, and the t-test were conducted. Logistic regression analyses were performed to determine possible demographic (sex, age, and education), clinical factors (course, age at onset of MS, disease duration, EDSS score) and MS symptoms (fatigue, clinically relevant depressive and apathetic symptoms) associated with patients’ unemployment status (dependent variable). BDI scores higher than 14 were used to identify patients with clinically relevant depressive symptoms, while AES-S score higher than 35.5 identified clinically relevant apathetic symptoms.

Pearson’s correlation was used to investigate the relation between WSAS composite scores and main demographical and clinical variables. According to widely used criteria, r values were interpreted as follows: r values > 0.7 strong, 0.3±0.7 moderate, <0.3 weak. The t-test was used to assess the different effects of visible (physical disability) and invisible (clinically relevant depressive and apathetic symptoms and fatigue) MS symptoms on WSAS scores. For all assays, statistical significance was set at P < 0.05.

4 Results

This study evaluated 123 pwMS, of whom 69.1% (n = 85) were female; a relapsing course was reported for 83.7% (n = 103) participants. Mean values for age and disease duration were 37.9 and 9.2 years, respectively, while the mean EDSS score was 2.4. Four pwMS were excluded owing to demonstrated cognitive impairment. Table 1 displays demographic and MS characteristics of all pwMS, including mean AES-S, BDI-II, and FSS scores.

Table 1
Demographic and clinical characteristics of the MS sample (n = 123)

Demographic Data MS Patients (123)

Sex

Female 85 (69.1%)

Age (years±sd) 37.9±9

Education (years±sd) 12.4±3.9

Clinical data

Disease course

Relapsing MS 103 (83.7%)

Age at onset 28.7±8.8

Disease duration (years±sd) 9.2±6

EDSS (mean±sd) 2.4±1.8

EDSS < 4 102

EDSS 4–6 14

EDSS > 6 7

AES-S score (mean±sd) 31±6.4

BDI-II (mean±sd) 8.2±8.6

FSS (mean±sd) 4±3.4

Demographic Data	MS Patients (123)
Sex
Female	85 (69.1%)
Age (years±sd)	37.9±9
Education (years±sd)	12.4±3.9
Clinical data
Disease course
Relapsing MS	103 (83.7%)
Age at onset	28.7±8.8
Disease duration (years±sd)	9.2±6
EDSS (mean±sd)	2.4±1.8
EDSS < 4	102
EDSS 4–6	14
EDSS > 6	7
AES-S score (mean±sd)	31±6.4
BDI-II (mean±sd)	8.2±8.6
FSS (mean±sd)	4±3.4

Abbreviation: MS: Multiple Sclerosis; EDSS: Expanded Disability Status Scale; AES-S: Apathy Evaluation Scale; BDI-II: Beck Depression Inventory-Second Edition; FSS: Fatigue Severity Scale.

At the time of the study, 51.2 % (n = 63) pwMS were employed with 11.1% (n = 7) owing to employment protection legislation. In contrast, 42.3% (n = 52) were unemployed. Of the participants, 14.6% (n = 18), 9 of whom were unemployed, were receiving disability pension, whereas 4.9% (n = 6) were receiving family financial aid. Table 2 reports participants’ employment status, type of job contract, and financial assistance.

Table 2

Employment status, type of job contract, and financial assistance in the MS sample

Employment Status	MS Patients (123)
Employed	63 (51.2%)
Unemployed	52 (42.3%)
Students	7 (5.7%)
Retirees	1 (0.8%)
Type of Job Contract	Employed Patients (63)
Permanent contract	35 (55.5%)
Fixed-term contract	10 (15.9%)
Others	18 (28.6%)
Financial Assistance
Unemployment allowance	1 (0.8%)
Scholarship	4 (3.3 %)
Disability pension	18 (14.6%)
Financial aid by family	6 (4.9%)
No financial assistance	94 (74.4%)

Logistic regression analysis indicated that employment status was positively associated with a higher education level (p = 0.01), whereas it was negatively associated with female gender (p = 0.03). It was also found to be negatively associated with higher disability (p = 0.02); in particular, a one-point increase in EDSS scores reduced the possibility of employment to one-third (Table 3). No associations were found between employment and disease course or duration, age at onset of MS, and presence of fatigue or clinically relevant depressive and apathetic symptoms (Table 3).

Table 3

Logistic regression analyses with demographic and clinical variables associated with unemployment status

	B	Sig.	Exp (B)	95% C.I. for EXP (B)
				Lower	Upper
Variables
Female gender	–1.018	0.038*	0.361	0.138	0.947
Age	0.005	0.847	1.005	0.955	1.057
Education	0.143	0.013*	1.154	1.031	1.291
Age at onset of MS	–0.017	0.668	0.984	0.912	1.061
Disease duration	0.022	0.572	1.022	0.947	1.104
EDSS score	–0.399	0.021*	0.671	0.478	0.943
Apathy	–0.142	0.772	0.868	0.332	2.269
Depression	–0.492	0.365	0.611	0.211	1.773
Fatigue	742	0.179	2.101	0.712	6.203

a. Dependent variable: unemployment status. b. Independent Variable(s) entered: Female gender, age, education, age at onset, disease duration, EDSS score, and occurrence of apathy, depression, and fatigue. MS patients with depressive symptomatology are identified by BDI-II scores greater than 14, patients with fatigue by FSS scores greater than 4, patients with apathetic symptoms by AES-S score greater than 35.

Descriptive analyses of cohabitation status (with family of origin, partners/children or alone), social interaction (daily, weekly or rarely meeting friends), and recreational activities (alone or in group) are shown in Table 4. Impairment in social interactions was reported in 15 (12.2%) patients that rarely meet friends while no recreational activities were reported in 88 (71.5%) patients.

Table 4

Cohabitation status, social interaction, and recreational activities in the MS sample

Cohabitation Status	MS Patients (123)
Family of origin	36 (29.2%)
Partner/children	61 (49.6%)
Alone	13 (10.6%)
Others	13 (10.6%)
Social Interactions
Daily meetings with friends	63 (51.2%)
Weekly meetings with friends	45 (36.6%)
Rarely meeting friends	15 (12.2%)
Recreational Activities	35 (28.5%)
In groups	11 (9%)
Alone	24 (19.5%)
No recreational activities	88 (71.5%)

Regarding the impact of MS on daily activities captured by WSAS questionnaire, Table 5 show that WSAS score was significantly correlated with EDSS scores (r = 0.565, p < 0.001), BDI-II scores (r = 0.588, p < 0.001), and FSS scores (r = 0.545, p < 0.001). Correlations between each WSAS composite score domain (ability to work, home management, social and leisure activities, and close relationships) and MS clinical features are also showed (Table 5).

Table 5

Pearson’s Correlation between age, disease duration, EDSS, FSS, BDI-II, and AES-S scores and WSAS composite score, including daily functioning (ability to work, home management, social and leisure activities, and close relationships)

	Ability to work	Home management	Social activities	Leisure activities	Close relationships	WSAS total score
Age	r = 316, p < 0.001	r = 410, p < 0.001	r = 278, p = 0.002	r = 245, p = 0.006	r = 260, p = 0.004	r = 356, p < 0.001
Disease duration	r = 278, p < 0.002	r = 306, p = 0.001	r = 280, p = 0.002	r = 314, p < 0.001	r = 219, p = 0.015	r = 332, p < 0.001
EDSS score	r = 595, p < 0.001	r = 589, p < 0.001	r = 448, p < 0.001	r = 427, p < 0.001	r = 308, p < 0.001	r = 565, p < 0.001
AES-S score	r = 251, p = 0.005	r = 306, p = 0.001	r = 408, p < 0.001	r = 334, p < 0.001	r = 391, p < 0.001	r = 392, p < 0.001
BDI-II score	r = 396, p < 0.001	r = 478, p < 0.001	r = 530, p < 0.001	r = 517, p < 0.001	r = 611, p < 0.001	r = 588, p < 0.001
FSS score	r = 484, p < 0.001	r = 465, p < 0.001	r = 441, p < 0.001	r = 537, p < 0.001	r = 367, p < 0.001	r = 545, p < 0.001

R values were interpreted according to widely used criteria: r values > 0.7 strong, 0.3±0.7 moderate, < 0.3 weak. Sig. (2-tailed). Abbreviation: EDSS: Expanded Disability Status Scale; AES-S: Apathy Evaluation Scale; BDI-II: Beck Depression Inventory-Second Edition; FSS: Fatigue Severity Scale.

Table 6 shows the attributable burden due to MS disability, depression, apathy, and fatigue in increasing the WSAS score. MS patients with depressive symptomatology are identified by BDI-II scores greater than 14, patients with fatigue by FSS scores greater than 4, patients with apathetic symptoms by AES-S score greater than 35. The EDSS score 4 was chosen as it represents the achievement of a measurable limitation (500 meters) in the ambulation. The t-test reported significant differences of WSAS scores between MS patients with depression vs patients without depression, patients with fatigue vs patients without fatigue and patients with apathy vs patients without apathy. Attributable burden due to MS was calculated as the difference between the WSAS score of those pwMS who, along with MS, suffered from depression, apathy, fatigue, or higher disability (EDSS > 4) versus those suffering from MS. As shown in Table 6, the perception of the MS burden reported by pwMS with visible physical disability, indicated by an EDSS score greater than 4, was more relevant as compared with that reported by pwMS who experienced depression, apathy, or fatigue.

Table 6

Attributable burden calculated as the difference between the impairment of WSAS score of those patients who, along with MS, suffered from depression, apathy, fatigue, or higher disability vs the impairment of WSAS score in those suffering from MS

	MS Patients
	With Depression	Without Depression	Attributable Burden
Work and Social Score	15.5±8.1	5.5±8.1*	10
	With Fatigue	Without Fatigue	Attributable Burden
	15.5±8.9	4.8±7.5*	10.7
	With Apathy	Without Apathy	Attributable Burden
	15.1±11	6.8±8.2*	8.3
	EDSS > 4	EDSS < 4	Attributable Burden
∼	19.4±8.2	6.7±8.4*	12.7

MS patients with depressive symptomatology are identified by BDI-II scores greater than 14, patients with fatigue by FSS scores greater than 4, patients with apathetic symptoms by AES-S score greater than 35.5. The t-test was used to assess differences of the WSAS score between MS patients with depression vs patients without depression, patients with fatigue vs patients without fatigue, patients with EDSS < 4 vs patients with EDSS > 4, patients with apathy vs patients without apathy. *P < 0.01

5 Discussion

MS is frequently diagnosed in young adulthood and, given its chronic, unpredictable, and long-term nature, has several implications for patients’ private and working lives [1]. Our study was performed in Sardinia, an island where peculiar genetic and environmental factors act together through a complex interaction [26, 27], resulting in an increased risk and frequency of MS.

Our study explored the impact of MS on daily functioning, with particular attention to working life and employment status in order to identify which clinical features are related to unemployment status among adults with MS. The 2015 local unemployment rate in the general Sardinian population (15–65 years) was 16.5%, with the highest rate being 17.9% for the female population [28]. Our results were consistent with those reported by other Italian studies, [29] showing that around 40% of all pwMS are unemployed. Therefore, we may conclude that MS is responsible for their unemployment status despite existing national employment protection legislation, which, in our sample, benefitted only 11.1% (n = 7) of pwMS with an employment. In the present study, we examined demographic and clinical features, confirming that, as in the general population, higher education levels are associated with employment status, while females are more prone to be unemployed [28]. Consistent with other studies, we found that disability level, assessed using EDSS scores, predicts unemployment status [4 , 31]. However, this scale considers predominantly motor and ambulation disability, thus it is only partially able to capture the hidden disabilities of MS [18]. For this reason we used WSAS questionnaire [23] that is more able to describe daily functioning as ability to work, home management, social and leisure activities, and close relationships, then all activities that can be influenced by both visible and invisible symptoms. Our study found as expected that EDSS scores significantly correlated with WSAS scores, indicating greater impairment in daily functioning in case of higher disability levels, especially in the ability to work (r = 0.565, p < 0.001). This result has particular implications for upgrading the functional working domain. In fact, it becomes an institutional duty to provide disabled pwMS with specific accommodations to maintain employment as long as possible, especially considering that loss of employment produces detrimental consequences in economic and, subsequently, psychosocial domains for both pwMS and their families. Depression and fatigue seem to principally affect social life, close relationships, and leisure activities of pwMS, potentially inducing a negative effect on their quality of life, as is well documented in several studies [32 –34]. Contrary to other studies that found fatigue [35, 36] and depression [37, 38] significantly associated with unemployment, no such associations were reported in our sample. However, depression studies have produced inconsistent results, while the results of one study are consistent with ours, [30] others indicate higher depression scores in employed than in unemployed groups [10]. Notoriously, depression is one of the most common invisible MS symptoms, [39] having several implications for disease management. In fact, the co-occurrence of depression can influence coping strategies, [5] resulting in maladaptive coping that might increase the overall perceived MS burden, at work as well as in private life [40, 41]. A recent study found that coping styles distinguished those who were employed from those who left work due to MS, [42] suggesting that helping pwMS cope should be emphasized. Therefore, it becomes important to recognize and treat pwMS with depression and, in particular, provide a global support system with focused psychosocial interventions aimed to improve coping mechanisms and compensatory strategies.

At least three of this study’s limitations must be mentioned. First, the study evaluated employment status in MS patients by cross-sectional design. However, employment status is a dynamic process with frequent transitions; thus pwMS can leave and return to work. Furthermore, marriage, pregnancy, and other non-MS factors can also determine job loss in the MS population, just as in the healthy population. Therefore, this study only partially captured the work status of pwMS, further highlighting the need for more focused quantitative and qualitative studies on this issue. In the same way, WSAS scores can only partially reveal perception of the MS burden, thus further studies are indispensable to better evaluate this perception.

The second limitation concerns the relatively low EDSS scores of pwMS included in this study; however, their inclusion was random, following their order of arrival at the MS Center. Nonetheless, the sample is sufficiently representative of the young-adult MS population. Finally, our sample, as declared in the methods section, did not include pwMS with cognitive impairment. Therefore, we could not evaluate whether cognitive impairment influenced participants’ employment status and this factor’s impact on worsening daily functioning over time.

6 Conclusion

Our study revealed the important role of physical disability in determining unemployment in pwMS. Alternatively, invisible MS symptoms negatively and specifically affect daily functioning, principally affecting patients’ social life, close relationships, and leisure activities. Thus these, as other invisible symptoms for which validated instruments are usable, [43, 44] should be readily classified and focused programs with psychosocial interventions should be designed to facilitate the acceptance of pwMS at work and to improve the social integration and daily activities of pwMS.

Conflict of interest

There is no conflict of interest regarding the present paper. No funding was received to realize this study.

Ethical approval

Informed consent was obtained from all individual participants included in the study.

References

Mitchell

, Benito-León

, González

, Rivera-Navarro

. Quality of life and its assessment in multiple sclerosis: Integrating physical and psychological components of wellbeing. Lancet Neurol 2005;4(9):556–66.

Cocco

, Sardu

, Spinicci

, Musu

, Massa

, Frau

, et al. Influence of treatments in multiple sclerosis disability: A cohort study. Mult Scler 2015;21(4):433–41.

Melis

, Cocco

, Frau

, Lorefice

, Fenu

, Coghe

, et al. Post-natalizumab clinical and radiological findings in a cohort of multiple sclerosis patients: 12-month follow-up. Neurol Sci 2014;35(3):401–8.

, Fitzgerald

, Bishop

, Rumrill

, Wang

. Disease-related and functional predictors of employment status among adults with multiple sclerosis. Work 2015;52(4):789–97.

Lorefice

, Fenu

, Frau

, Coghe

, Marrosu

, Cocco

. The burden of multiple sclerosis and patients’ coping strategies. BMJ Support Palliat Care 2017.

Neath

, Roessler

, McMahon

, Rumrill

. Patterns in perceived employment discrimination for adults with multiple sclerosis. Work 2007;29(3):255–74.

Carrieri

, Sgaramella

, Bortolon

, Stenta

, Fornaro

, Cracco

, et al. Determinants of on-the-job-barriers in employed persons with multiple sclerosis: The role of disability severity and cognitive indices. Work 2014;47(4):509–20.

Bishop

, Rumrill

. Multiple sclerosis: Etiology, symptoms, incidence and prevalence, and implications for community living and employment. Work 2015;52(4):725–34.

Busche

, Fisk

, Murray

, Metz

. Short term predictors of unemployment in multiple sclerosis patients. Can J Neurol Sci 2003;30(2):137–42.

10.

Smith

, Arnett

. Factors related to employment status changes in individuals with multiple sclerosis. Mult Scler 2005;11(5):602–9.

11.

Grytten

, Måseide

. ‘What is expressed is not always what is felt’: Coping with stigma and the embodiment of perceived illegitimacy of multiple sclerosis. Chronic Illn 2005;1(3):231–43.

12.

Lorefice

, Mura

, Coni

, Fenu

, Sardu

, Frau

, et al. What do multiple sclerosis patients and their caregivers perceive as unmet needs? BMC Neurol 2013;13:177.

13.

Hakim

, Bakheit

AMO

, Bryantoe

, Roberts

MWH

, McIntosh-Michaelis

, Spackman

, et al. The social impact of multiple sclerosis—a study of 305 patients and their relatives. Disabil Rehabil 2000;22(6):288–93.

14.

Aikens

, Fischer

, Namey

, Rudick

. A replicated prospective investigation of life stress, coping, and depressive symptoms in multiple sclerosis. J Behav Med 1997;20(5):433–45.

15.

Cocco

, Sardu

, Massa

, Mamusa

, Musu

, Ferrigno

, et al. Epidemiology of multiple sclerosis in south-western Sardinia. Mult Scler 2011;17(11):1282–9.

16.

Cocco

, Sardu

, Lai

, Spinicci

, Contu

, Marrosu

. Anticipation of age at onset in multiple sclerosis: A Sardinian cohort study. Neurology 2004;62(10):1794–8.

17.

Polman

, Reingold

, Banwell

, Clanet

, Cohen

, Filippi

, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69(2):292–302.

18.

Kurtzke

. Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 1983;33(11):1444–52.

19.

Krupp

, LaRocca

, Muir-Nash

, Steinberg

. The fatigue severity scale: Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989;46(10):1121–3.

20.

Goldman Consensus Group. The Goldman Consensus statement on depression in multiple sclerosis. Mult Scler 2005;11(3):328–37.

21.

Beck

, Steer

, Brown

. Beck Depression Inventory: Manual. The Psychological Corporation, San Antonio, Tex, USA, 2nd edition, 1996.

22.

Raimo

, Trojano

, Spitaleri

, Petretta

, Grossi

, Santangelo

. Apathy in multiple sclerosis: A validation study of the apathy evaluation scale. J Neurol Sci 2014;347(1-2):295–300.

23.

Mundt

, Marks

, Shear

, Greist

. The Work and Social Adjustment Scale: A simple measure of impairment in functioning. Br J Psychiatry 2002;180:461–4.

24.

Rossi

, Rucci

, Mauri

, Maina

, Pieraccini

, Pallanti

, et al. Validity and reliability of the Italian version of the Quality of Life, Enjoyment and Satisfaction Questionnaire. Qual Life Res 2005;14(10):2323–8.

25.

Boringa

, Lazeron

, Reuling

, Adèr

, Pfennings

, Lindeboom

, et al. The brief repeatable battery of neuropsychological tests: Normative values allow application in multiple sclerosis clinical practice. Mult Scler 2001;7(4):263–7.

26.

Cocco

, Meloni

, Murru

, Corongiu

, Tranquilli

, Fadda

, et al. Vitamin D responsive elements within the HLA-DRB1 promoter region in Sardinian multiple sclerosis associated alleles. PLoS One 2012;7(7):e41678.

27.

Cocco

, Sardu

, Murru

, Frau

, Lorefice

, Mamusa

, et al. Multiple sclerosis risk: Interaction between human leukocyte antigen and the environment in Sardinian population. Mult Scler 2009;15(9):1030–6.

28.

http://www.istat.it/it/sardegna

29.

Solari

, Radice

. Health status of people with multiple sclerosis: A community mail survey. Neurol Sci 2001;22(4):307–15.

30.

Cadden

, Arnett

. Factors Associated with Employment Status in Individuals with Multiple Sclerosis. Int J MS Care 2015;17(6):284–91.

31.

Honarmand

, Akbar

, Kou

, Feinstein

. Predicting employment status in multiple sclerosis patients: The utility of the MS functional composite. J Neurol 2011;258(2):244–9.

32.

Spain

, Tubridy

, Kilpatrick

, Adams

, Holmes

. Illness perception and health-related quality of life in multiple sclerosis. Acta Neurol Scand 2007;116(5):293–9.

33.

Patti

, Cacopardo

, Palermo

, Ciancio

, Lopes

, Restivo

, et al. Health-related quality of life and depression in an Italian sample of multiple sclerosis patients. J Neurol Sci 2003;211(1-2):55–62.

34.

Amato

, Ponziani

, Rossi

, Liedl

, Stefanile

, Rossi

. Quality of life in multiple sclerosis: The impact of depression, fatigue and disability. Mult Scler 2001;7(5):340–4.

35.

Larocca

, Kalb

, Scheinberg

, Kendall

. Factors associated with unemployment of patients with multiple sclerosis. J Chronic Dis 1985;38(2):203–10.

36.

Messmer Uccelli

, Specchia

, Battaglia

, Miller

. Factors that influence the employment status of people with multiple sclerosis: A multi-national study. J Neurol 2009;256(12):1989–96.

37.

Glanz

, Dégano

, Rintell

, Chitnis

, Weiner

, Healy

. Work productivity in relapsing multiple sclerosis: Associations with disability, depression, fatigue, anxiety, cognition, and health-related quality of life. Value Health 2012;15(8):1029–35.

38.

Honarmand

, Akbar

, Kou

, Feinstein

. Predicting employment status in multiple sclerosis patients: The utility of the MS functional composite. J Neurol 2011;258(2):244–9.

39.

Patten

, Beck

, Williams

, Barbui

, Metz

. Major depression in multiple sclerosis: A population-based perspective. Neurology 2003;61(11):1524–7.

40.

Goretti

, Portaccio

, Zipoli

, Razzolini

, Amato

. Coping strategies, cognitive impairment, psychological variables and their relationship with quality of life in multiple sclerosis. Neurol Sci 2010;31(Suppl 2):S227–30.

41.

McCabe

, McKern

, McDonald

. Coping and psychological adjustment among people with multiple sclerosis. J Psychosom Res 2004;56(3):355–61.

42.

Strober

, Arnett

. Unemployment among women with multiple sclerosis: The role of coping and perceived stress and support in the workplace. Psychol Health Med 2015:1–9.

43.

Solaro

, Rezzani

, Trabucco

, Amato

, Zipoli

, Portaccio

, et al. Prevalence of patient-reported dysphagia in multiple sclerosis patients: An Italian multicenter study (using the DYMUS questionnaire). J Neurol Sci 2013;331(1-2):94–7.

44.

Bergamaschi

, Rezzani

, Minguzzi

, Amato

, Patti

, Marrosu

, et al. Validation of the DYMUS questionnaire for the assessment of dysphagia in multiple sclerosis. Funct Neurol 2009;24(3):159–62.